Low-level contrast statistics are diagnostic of invariance of natural textures

Texture may provide important clues for real world object and scene perception. To be reliable, these clues should ideally be invariant to common viewing variations such as changes in illumination and orientation. In a large image database of natural materials, we found textures with low-level contrast statistics that varied substantially under viewing variations, as well as textures that remained relatively constant. This led us to ask whether textures with constant contrast statistics give rise to more invariant representations compared to other textures. To test this, we selected natural texture images with either high (HV) or low (LV) variance in contrast statistics and presented these to human observers. In two distinct behavioral categorization paradigms, participants more often judged HV textures as “different” compared to LV textures, showing that textures with constant contrast statistics are perceived as being more invariant. In a separate electroencephalogram (EEG) experiment, evoked responses to single texture images (single-image ERPs) were collected. The results show that differences in contrast statistics correlated with both early and late differences in occipital ERP amplitude between individual images. Importantly, ERP differences between images of HV textures were mainly driven by illumination angle, which was not the case for LV images: there, differences were completely driven by texture membership. These converging neural and behavioral results imply that some natural textures are surprisingly invariant to illumination changes and that low-level contrast statistics are diagnostic of the extent of this invariance

Representational geometry: integrating cognition, computation, and the brain

The cognitive concept of representation plays a key role in theories of brain information processing. However, linking neuronal activity to representational content and cognitive theory remains challenging. Recent studies have characterized the representational geometry of neural population codes by means of representational distance matrices, enabling researchers to compare representations across stages of processing and to test cognitive and computational theories. Representational geometry provides a useful intermediate level of description, capturing both the information represented in a neuronal population code and the format in which it is represented. We review recent insights gained with this approach in perception, memory, cognition, and action. Analyses of representational geometry can compare representations between models and the brain, and promise to explain brain computation as transformation of representational similarity structure

The Role of Diagnostic Objects in the Temporal Dynamics of Visual Scene Categorization

When we look at the world around us we are able to effortlessly categorize scenes, but it is still unclear what mechanisms we use to do so. Categorization could be driven by objects, low-level features, or a mixture of both. This study investigated the ways in which diagnostic objects (those found nearly exclusively in one scene category) contribute to scene categorization. It paired Electroencephalography (EEG) with machine learning classification to provide detailed temporal information about when categorization occurs. While recording EEG, participants categorized real-world photographs as one of three indoor scene types (bathroom, kitchen, office). They were shown either original images or versions where diagnostic or random objects had been obscured via localized Fourier phase randomization. EEG voltages and the independent components (ICs) of a whole brain independent component analysis (ICA) were used as feature vectors for a linear support vector machine (SVM) classifier to determine time-resolved accuracy. There were no significant differences in decoding accuracy between categories or between diagnostic and random conditions. Poor classifier performance is likely due to a lack of power, or overfitting of the model. It could also reflect unclear EEG-based neural correlates of each scene type due to the inherent similarities in the categories. While the lack of significant decoding makes it difficult to make strong conclusions about the role of diagnostic objects in visual scene categorization, this study addresses important considerations for pairing EEG with decoding techniques and highlights some of the broader difficulties of isolating distinct features of visual scenes

Representational maps in the brain: concepts, approaches, and applications

Neural systems have evolved to process sensory stimuli in a way that allows for efficient and adaptive behavior in a complex environment. Recent technological advances enable us to investigate sensory processing in animal models by simultaneously recording the activity of large populations of neurons with single-cell resolution, yielding high-dimensional datasets. In this review, we discuss concepts and approaches for assessing the population-level representation of sensory stimuli in the form of a representational map. In such a map, not only are the identities of stimuli distinctly represented, but their relational similarity is also mapped onto the space of neuronal activity. We highlight example studies in which the structure of representational maps in the brain are estimated from recordings in humans as well as animals and compare their methodological approaches. Finally, we integrate these aspects and provide an outlook for how the concept of representational maps could be applied to various fields in basic and clinical neuroscience

The speed of visual processing of complex objects in the human brain. Sensitivity to image properties, the influence of aging, optical factors and individual differences.

Visual processing of complex objects is a feat that the brain accomplishes with remarkable speed – generally in the order of a few hundred milliseconds. Our knowledge with regards to what visual information the brain uses to categorise objects, and how early the first object-sensitive responses occur in the brain, remains fragmented. It seems that neuronal processing speed slows down with age due to a variety of physiological changes occurring in the aging brain, including myelin degeneration, a decrease in the selectivity of neuronal responses and a reduced efficiency of cortical networks. There are also considerable individual differences in age-related alterations of processing speed, the origins of which remain unclear. Neural processing speed in humans can be studied using electroencephalogram (EEG), which records the activity of neurons contained in Event-Related-Potentials (ERPs) with millisecond precision. Research presented in this thesis had several goals. First, it aimed to measure the sensitivity of object-related ERPs to visual information contained in the Fourier phase and amplitude spectra of images. The second goal was to measure age-related changes in ERP visual processing speed and to find out if their individual variability is due to individual differences in optical factors, such as senile miosis (reduction in pupil size with age), which affects retinal illuminance. The final aim was to quantify the onsets of ERP sensitivity to objects (in particular faces) in the human brain. To answer these questions, parametric experimental designs, novel approaches to EEG data pre-processing and analyses on a single-subject and group basis, robust statistics and large samples of subjects were employed. The results show that object-related ERPs are highly sensitive to phase spectrum and minimally to amplitude spectrum. Furthermore, when age-related changes in the whole shape of ERP waveform between 0-500 ms were considered, a 1 ms/year delay in visual processing speed has been revealed. This delay could not be explained by individual variability in pupil size or retinal illuminance. In addition, a new benchmark for the onset of ERP sensitivity to faces has been found at ~90 ms post-stimulus in a sample of 120 subjects age 18-81. The onsets did not change with age and aging started to affect object-related ERP activity ~125-130 ms after stimulus presentation. Taken together, this thesis presents novel findings with regards to the speed of visual processing in the human brain and outlines a range of robust methods for application in ERP vision research